Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis

NCT ID: NCT04220424

Last Updated: 2021-02-08

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-11-01
• Study Completion Date: 2022-03-01

Brief Summary

This prospective study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology and genetic sequencing data. By leveraging artificial intelligence, this registry seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.

Detailed Description

Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, and patients survival is challenging for gliomas. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging), and in the histopathology images of HE slices of gliomas could be excavated to aid prediction of molecular pathology and patients' survival of gliomas. This study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and genetic data (Whole exome sequencing, RNA sequencing, proteomics, etc), and seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.

Conditions

Glioma

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

MR and Histopathology images based prediction of molecular pathology and patient survival

MR and Histopathology images based prediction of molecular pathology and patient survival in gliomas by leverage artificial intelligence algorithms

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Patients must have radiologically and histologically confirmed diagnosis of primary glioma
• Life expectancy of greater than 3 months
• Must receive tumor resection
• Must have sufficient frozen tissues and peripheral blood samples for sequencing
• Must have high-quality MR images and histopathology images
• Signed informed consent

Exclusion Criteria

• No gliomas
• No sufficient amount of tumor tissues for detection of molecular pathology
• Patients who are pregnant or breast feeding
• Patients who are suffered from severe systematic malfunctions

• Minimum Eligible Age: 1 Year
• Maximum Eligible Age: 95 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Sun Yat-sen University

OTHER

Sponsor Role: collaborator

The First Affiliated Hospital of Zhengzhou University

OTHER

Sponsor Role: lead

Responsible Party

Zhenyu Zhang

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University

Zhengzhou, Henan, China

Site Status: RECRUITING

Countries

China

Central Contacts

Zhenyu Zhang

Role: CONTACT

fcczhangzy1@zzu.edu.cn

+86 17839973727

Facility Contacts

Zhenyu Zhang, Dr.

Role: primary

fcczhangzy1@zzu.edu.cn

+86 17839973727

Other Identifiers

GliomaAI-6

Identifier Type: -

Identifier Source: org_study_id